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Dec 13, 2019 · Recently, self-attention has drawn an enormous amount of attention in natural language processing (NLP). Transformer [NIPS2017_7181], a fully self-attention framework, has been widely adopted in many state-of-the-art pre-training language models [devlin_2018, radford2019language, xlnet].

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Mar 23, 2021 · This notebook demonstrates how to train a Variational Autoencoder (VAE) (1, 2) on the MNIST dataset.A VAE is a probabilistic take on the autoencoder, a model which takes high dimensional input data and compresses it into a smaller representation.

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I am a Ph.D. student at Fudan University advised by Prof. Junping Zhang.Previously, I got my bachelor's degree from Sichuan University in 2019. My main research interests include generative model and biometrics, especially face-related applications.

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terns with attention based ConvLSTM networks, and re-construct signature matrix via a convolutional decoder. As far as we know, MSCRED is the first model that considers correlations among multivariate time series for anomaly detection and can jointly resolve all the three tasks. We conduct extensive empirical studies on a synthetic

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時系列データ解析の為にRNNを使ってみようと思い,簡単な実装をして,時系列データとして ほとんど,以下の真似ごとなのでいいねはそちらにお願いします. 深層学習ライブラリKerasでRNNを使ってsin波予測 LSTM で正弦波を予測す...

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Jan 01, 2021 · To address this issue, we further design the Bi-ConvLSTM f Bi-ConvLSTM to maintain long-term visual attention stability, generating the enhanced spatiotemporal representation as: (3) {H t f, H t b} = f Bi − ConvLSTM ({A t, H t − 1 f, H t + 1 b}; θ Bi − ConvLSTM), where H t f and H t b denotes the forwardly and backwardly estimated hidden ...

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Jun 24, 2020 · Based on multi-scale network and ConvLSTM,Chu et al.[4] proposed the MultiConvLSTM, which can extract temporal and spatial characteristics from historical data and metadata to predict the traffic of origin–destination .

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Oct 19, 2020 · Attention Augmented ConvLSTM for ... results from this paper to get state-of-the-art GitHub badges and help the ... ConvLSTM Recurrent Neural Networks ...

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of traffic flow with an attention mechanism. In particular, our ATFM is composed of two progressive Convolutional Long Short-Term Memory (ConvLSTM [1]) units connected with a convolutional layer. Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden

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Jun 03, 2014 · In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional ...

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I am a Ph.D. student at Fudan University advised by Prof. Junping Zhang.Previously, I got my bachelor's degree from Sichuan University in 2019. My main research interests include generative model and biometrics, especially face-related applications.

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Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

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Sequence-to-sequence models have shown success in end-to-end speech recognition. However these models have only used shallow acoustic encoder networks. In our work, we successively train very deep convolutional networks to add more expressive power and better generalization for end-to-end ASR models. We apply network-in-network principles, batch normalization, residual connections and ...

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回答 1 已采纳 使用ConvLSTM预测图像序列总是出现过拟合的问题,数据集是Moving MNIST 移动数据集,前10帧预测后10帧,训练集11000个序列,验证集1000个序列,优化器Adam,学习率0.001,batch_size=10,MSE做损失函数。 已尝试增加normalization、dropout、梯度裁剪、L1与L2正则10e ... Liu Zihao, Attention Based Glaucoma Detection: A Large-scale Database and CNN Model, CVPR 2019. April 30, 2020: Paper Presentation - II: Yan Zuoyu, Asymmetric Non-local Neural Networks for Semantic Segmentation, CVPR 2019. Zhou Xinzhe, Adversarial Examples Are Not Bugs, They Are Features, NIPS2019. Jiang Borui, Designing Network Design Spaces ...

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Biological inspirations: Humans do not perceive a whole scene at once, instead they focus attention on parts of the visual space to acquire information and then combine it to build an internal representation of the scene. Locations at which humans fixate have been shown to be task specific. Recurrent Attention Model (R

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A Data Driven Graph Generative Model for Temporal Interaction Networks Authors: Dawei Zhou: University of Illinois at Urbana-Champaign; Lecheng Zheng: University of Illinois at Urbana-Champaign; Jiawei Han: University of Illinois at Urbana-Champaign; Jingrui He: University of Illinois at Urbana-Champaign

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